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<a href="#pub-types">Public 类型</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pro-methods">Protected 成员函数</a> &#124;
<a href="classpcl_1_1_sample_consensus_model_line-members.html">所有成员列表</a>  </div>
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<div class="title">pcl::SampleConsensusModelLine&lt; PointT &gt; 模板类 参考</div>  </div>
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<p><a class="el" href="classpcl_1_1_sample_consensus_model_line.html" title="SampleConsensusModelLine defines a model for 3D line segmentation. The model coefficients are defined...">SampleConsensusModelLine</a> defines a model for 3D line segmentation. The model coefficients are defined as:  
 <a href="classpcl_1_1_sample_consensus_model_line.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="sac__model__line_8h_source.html">sac_model_line.h</a>&gt;</code></p>
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类 pcl::SampleConsensusModelLine&lt; PointT &gt; 继承关系图:</div>
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 <div class="center">
  <img src="classpcl_1_1_sample_consensus_model_line.png" usemap="#pcl::SampleConsensusModelLine_3C_20PointT_20_3E_map" alt=""/>
  <map id="pcl::SampleConsensusModelLine_3C_20PointT_20_3E_map" name="pcl::SampleConsensusModelLine_3C_20PointT_20_3E_map">
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<area href="classpcl_1_1_sample_consensus_model_parallel_line.html" title="SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular co..." alt="pcl::SampleConsensusModelParallelLine&lt; PointT &gt;" shape="rect" coords="0,112,299,136"/>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public 类型</h2></td></tr>
<tr class="memitem:ad9a86731b46d7f45f5ee75e2546b5e6b"><td class="memItemLeft" align="right" valign="top"><a id="ad9a86731b46d7f45f5ee75e2546b5e6b"></a>
typedef <a class="el" href="classpcl_1_1_sample_consensus_model.html">SampleConsensusModel</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::<a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a>&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloud</b></td></tr>
<tr class="separator:ad9a86731b46d7f45f5ee75e2546b5e6b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac129bfa70dde9f6a7f1366eccc918a7d"><td class="memItemLeft" align="right" valign="top"><a id="ac129bfa70dde9f6a7f1366eccc918a7d"></a>
typedef <a class="el" href="classpcl_1_1_sample_consensus_model.html">SampleConsensusModel</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::PointCloudPtr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudPtr</b></td></tr>
<tr class="separator:ac129bfa70dde9f6a7f1366eccc918a7d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6bd6118658a68dab0cb09e74869f4313"><td class="memItemLeft" align="right" valign="top"><a id="a6bd6118658a68dab0cb09e74869f4313"></a>
typedef <a class="el" href="classpcl_1_1_sample_consensus_model.html">SampleConsensusModel</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudConstPtr</b></td></tr>
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<tr class="memitem:a69084e1ae492e146720c02f1d45dfdb3"><td class="memItemLeft" align="right" valign="top"><a id="a69084e1ae492e146720c02f1d45dfdb3"></a>
typedef boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_sample_consensus_model_line.html">SampleConsensusModelLine</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>Ptr</b></td></tr>
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<tr class="inherit_header pub_types_classpcl_1_1_sample_consensus_model"><td colspan="2" onclick="javascript:toggleInherit('pub_types_classpcl_1_1_sample_consensus_model')"><img src="closed.png" alt="-"/>&#160;Public 类型 继承自 <a class="el" href="classpcl_1_1_sample_consensus_model.html">pcl::SampleConsensusModel&lt; PointT &gt;</a></td></tr>
<tr class="memitem:a70b51e8d9f8526c1d2dc69ce68d9269e inherit pub_types_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a70b51e8d9f8526c1d2dc69ce68d9269e"></a>
typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloud</b></td></tr>
<tr class="separator:a70b51e8d9f8526c1d2dc69ce68d9269e inherit pub_types_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8f28a92c63418a12f95d1d668d13b49f inherit pub_types_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a8f28a92c63418a12f95d1d668d13b49f"></a>
typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::ConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudConstPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudPtr</b></td></tr>
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<tr class="memitem:ae432b503734f6bb16a0c47b6bfa8616d inherit pub_types_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="ae432b503734f6bb16a0c47b6bfa8616d"></a>
typedef <a class="el" href="classpcl_1_1search_1_1_search.html">pcl::search::Search</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>SearchPtr</b></td></tr>
<tr class="separator:ae432b503734f6bb16a0c47b6bfa8616d inherit pub_types_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acf6ca2b42beebcccb83a22d50866392c inherit pub_types_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="acf6ca2b42beebcccb83a22d50866392c"></a>
typedef boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_sample_consensus_model.html">SampleConsensusModel</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>Ptr</b></td></tr>
<tr class="separator:acf6ca2b42beebcccb83a22d50866392c inherit pub_types_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a028b710e8a2c3b3fa62c34c1a348c575 inherit pub_types_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a028b710e8a2c3b3fa62c34c1a348c575"></a>
typedef boost::shared_ptr&lt; const <a class="el" href="classpcl_1_1_sample_consensus_model.html">SampleConsensusModel</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>ConstPtr</b></td></tr>
<tr class="separator:a028b710e8a2c3b3fa62c34c1a348c575 inherit pub_types_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:a790ec0b14c1f834f1d5377999bfb0d6d"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#a790ec0b14c1f834f1d5377999bfb0d6d">SampleConsensusModelLine</a> (const PointCloudConstPtr &amp;cloud, bool random=false)</td></tr>
<tr class="memdesc:a790ec0b14c1f834f1d5377999bfb0d6d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor for base <a class="el" href="classpcl_1_1_sample_consensus_model_line.html" title="SampleConsensusModelLine defines a model for 3D line segmentation. The model coefficients are defined...">SampleConsensusModelLine</a>.  <a href="classpcl_1_1_sample_consensus_model_line.html#a790ec0b14c1f834f1d5377999bfb0d6d">更多...</a><br /></td></tr>
<tr class="separator:a790ec0b14c1f834f1d5377999bfb0d6d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abe1a6aab73b9d5674cccf1b042404975"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#abe1a6aab73b9d5674cccf1b042404975">SampleConsensusModelLine</a> (const PointCloudConstPtr &amp;cloud, const std::vector&lt; int &gt; &amp;indices, bool random=false)</td></tr>
<tr class="memdesc:abe1a6aab73b9d5674cccf1b042404975"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor for base <a class="el" href="classpcl_1_1_sample_consensus_model_line.html" title="SampleConsensusModelLine defines a model for 3D line segmentation. The model coefficients are defined...">SampleConsensusModelLine</a>.  <a href="classpcl_1_1_sample_consensus_model_line.html#abe1a6aab73b9d5674cccf1b042404975">更多...</a><br /></td></tr>
<tr class="separator:abe1a6aab73b9d5674cccf1b042404975"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aecf77e27258118a6db4b1290c99e72dc"><td class="memItemLeft" align="right" valign="top"><a id="aecf77e27258118a6db4b1290c99e72dc"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#aecf77e27258118a6db4b1290c99e72dc">~SampleConsensusModelLine</a> ()</td></tr>
<tr class="memdesc:aecf77e27258118a6db4b1290c99e72dc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty destructor <br /></td></tr>
<tr class="separator:aecf77e27258118a6db4b1290c99e72dc"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2c09de6c3d95758f25c1942ac7a050f9"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#a2c09de6c3d95758f25c1942ac7a050f9">computeModelCoefficients</a> (const std::vector&lt; int &gt; &amp;samples, Eigen::VectorXf &amp;model_coefficients)</td></tr>
<tr class="memdesc:a2c09de6c3d95758f25c1942ac7a050f9"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check whether the given index samples can form a valid line model, compute the model coefficients from these samples and store them internally in model_coefficients_. The line coefficients are represented by a point and a line direction  <a href="classpcl_1_1_sample_consensus_model_line.html#a2c09de6c3d95758f25c1942ac7a050f9">更多...</a><br /></td></tr>
<tr class="separator:a2c09de6c3d95758f25c1942ac7a050f9"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a68b282649799eac370616e9ab124c970"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#a68b282649799eac370616e9ab124c970">getDistancesToModel</a> (const Eigen::VectorXf &amp;model_coefficients, std::vector&lt; double &gt; &amp;distances)</td></tr>
<tr class="memdesc:a68b282649799eac370616e9ab124c970"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute all squared distances from the cloud data to a given line model.  <a href="classpcl_1_1_sample_consensus_model_line.html#a68b282649799eac370616e9ab124c970">更多...</a><br /></td></tr>
<tr class="separator:a68b282649799eac370616e9ab124c970"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1204c9998433ce3b8b1e20240085df66"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#a1204c9998433ce3b8b1e20240085df66">selectWithinDistance</a> (const Eigen::VectorXf &amp;model_coefficients, const double threshold, std::vector&lt; int &gt; &amp;inliers)</td></tr>
<tr class="memdesc:a1204c9998433ce3b8b1e20240085df66"><td class="mdescLeft">&#160;</td><td class="mdescRight">Select all the points which respect the given model coefficients as inliers.  <a href="classpcl_1_1_sample_consensus_model_line.html#a1204c9998433ce3b8b1e20240085df66">更多...</a><br /></td></tr>
<tr class="separator:a1204c9998433ce3b8b1e20240085df66"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2885d3c4271f8cb8aed6ebc34c65d994"><td class="memItemLeft" align="right" valign="top">virtual int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#a2885d3c4271f8cb8aed6ebc34c65d994">countWithinDistance</a> (const Eigen::VectorXf &amp;model_coefficients, const double threshold)</td></tr>
<tr class="memdesc:a2885d3c4271f8cb8aed6ebc34c65d994"><td class="mdescLeft">&#160;</td><td class="mdescRight">Count all the points which respect the given model coefficients as inliers.  <a href="classpcl_1_1_sample_consensus_model_line.html#a2885d3c4271f8cb8aed6ebc34c65d994">更多...</a><br /></td></tr>
<tr class="separator:a2885d3c4271f8cb8aed6ebc34c65d994"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad29efa5e1fc75c814612f5c4926233ef"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#ad29efa5e1fc75c814612f5c4926233ef">optimizeModelCoefficients</a> (const std::vector&lt; int &gt; &amp;inliers, const Eigen::VectorXf &amp;model_coefficients, Eigen::VectorXf &amp;optimized_coefficients)</td></tr>
<tr class="memdesc:ad29efa5e1fc75c814612f5c4926233ef"><td class="mdescLeft">&#160;</td><td class="mdescRight">Recompute the line coefficients using the given inlier set and return them to the user.  <a href="classpcl_1_1_sample_consensus_model_line.html#ad29efa5e1fc75c814612f5c4926233ef">更多...</a><br /></td></tr>
<tr class="separator:ad29efa5e1fc75c814612f5c4926233ef"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afc373efb74ec6e2c4761a5714b54b316"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#afc373efb74ec6e2c4761a5714b54b316">projectPoints</a> (const std::vector&lt; int &gt; &amp;inliers, const Eigen::VectorXf &amp;model_coefficients, <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &amp;projected_points, bool copy_data_fields=true)</td></tr>
<tr class="memdesc:afc373efb74ec6e2c4761a5714b54b316"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create a new point cloud with inliers projected onto the line model.  <a href="classpcl_1_1_sample_consensus_model_line.html#afc373efb74ec6e2c4761a5714b54b316">更多...</a><br /></td></tr>
<tr class="separator:afc373efb74ec6e2c4761a5714b54b316"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a46511a245836abd6d73fd924f2a4f285"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#a46511a245836abd6d73fd924f2a4f285">doSamplesVerifyModel</a> (const std::set&lt; int &gt; &amp;indices, const Eigen::VectorXf &amp;model_coefficients, const double threshold)</td></tr>
<tr class="memdesc:a46511a245836abd6d73fd924f2a4f285"><td class="mdescLeft">&#160;</td><td class="mdescRight">Verify whether a subset of indices verifies the given line model coefficients.  <a href="classpcl_1_1_sample_consensus_model_line.html#a46511a245836abd6d73fd924f2a4f285">更多...</a><br /></td></tr>
<tr class="separator:a46511a245836abd6d73fd924f2a4f285"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a13524285fe1745224bf8c35e6dbe2713"><td class="memItemLeft" align="right" valign="top"><a id="a13524285fe1745224bf8c35e6dbe2713"></a>
pcl::SacModel&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#a13524285fe1745224bf8c35e6dbe2713">getModelType</a> () const</td></tr>
<tr class="memdesc:a13524285fe1745224bf8c35e6dbe2713"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return an unique id for this model (SACMODEL_LINE). <br /></td></tr>
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<tr class="inherit_header pub_methods_classpcl_1_1_sample_consensus_model"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classpcl_1_1_sample_consensus_model')"><img src="closed.png" alt="-"/>&#160;Public 成员函数 继承自 <a class="el" href="classpcl_1_1_sample_consensus_model.html">pcl::SampleConsensusModel&lt; PointT &gt;</a></td></tr>
<tr class="memitem:aa99d99ea0457237eca3b2f8e9a39022b inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#aa99d99ea0457237eca3b2f8e9a39022b">SampleConsensusModel</a> (const PointCloudConstPtr &amp;cloud, bool random=false)</td></tr>
<tr class="memdesc:aa99d99ea0457237eca3b2f8e9a39022b inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor for base <a class="el" href="classpcl_1_1_sample_consensus_model.html" title="SampleConsensusModel represents the base model class. All sample consensus models must inherit from t...">SampleConsensusModel</a>.  <a href="classpcl_1_1_sample_consensus_model.html#aa99d99ea0457237eca3b2f8e9a39022b">更多...</a><br /></td></tr>
<tr class="separator:aa99d99ea0457237eca3b2f8e9a39022b inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3fd6d364bc52a69c6aff54691975b7aa inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a3fd6d364bc52a69c6aff54691975b7aa">SampleConsensusModel</a> (const PointCloudConstPtr &amp;cloud, const std::vector&lt; int &gt; &amp;indices, bool random=false)</td></tr>
<tr class="memdesc:a3fd6d364bc52a69c6aff54691975b7aa inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor for base <a class="el" href="classpcl_1_1_sample_consensus_model.html" title="SampleConsensusModel represents the base model class. All sample consensus models must inherit from t...">SampleConsensusModel</a>.  <a href="classpcl_1_1_sample_consensus_model.html#a3fd6d364bc52a69c6aff54691975b7aa">更多...</a><br /></td></tr>
<tr class="separator:a3fd6d364bc52a69c6aff54691975b7aa inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6fd82ae7fce7b90406608b8db249acd1 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a6fd82ae7fce7b90406608b8db249acd1"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a6fd82ae7fce7b90406608b8db249acd1">~SampleConsensusModel</a> ()</td></tr>
<tr class="memdesc:a6fd82ae7fce7b90406608b8db249acd1 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor for base <a class="el" href="classpcl_1_1_sample_consensus_model.html" title="SampleConsensusModel represents the base model class. All sample consensus models must inherit from t...">SampleConsensusModel</a>. <br /></td></tr>
<tr class="separator:a6fd82ae7fce7b90406608b8db249acd1 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2ae178816daf4f2c7af3a19d12324b77 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a2ae178816daf4f2c7af3a19d12324b77">getSamples</a> (int &amp;iterations, std::vector&lt; int &gt; &amp;samples)</td></tr>
<tr class="memdesc:a2ae178816daf4f2c7af3a19d12324b77 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a set of random data samples and return them as point indices.  <a href="classpcl_1_1_sample_consensus_model.html#a2ae178816daf4f2c7af3a19d12324b77">更多...</a><br /></td></tr>
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<tr class="memitem:aae3b06fbee5243f926d40f8115bd406c inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#aae3b06fbee5243f926d40f8115bd406c">setInputCloud</a> (const PointCloudConstPtr &amp;cloud)</td></tr>
<tr class="memdesc:aae3b06fbee5243f926d40f8115bd406c inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the input dataset  <a href="classpcl_1_1_sample_consensus_model.html#aae3b06fbee5243f926d40f8115bd406c">更多...</a><br /></td></tr>
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<tr class="memitem:a41226f6ed6de6db5ea190f265f520fc9 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a41226f6ed6de6db5ea190f265f520fc9"></a>
PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a41226f6ed6de6db5ea190f265f520fc9">getInputCloud</a> () const</td></tr>
<tr class="memdesc:a41226f6ed6de6db5ea190f265f520fc9 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the input point cloud dataset. <br /></td></tr>
<tr class="separator:a41226f6ed6de6db5ea190f265f520fc9 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4d9b6652b63eacd8c0ae1fb3a3eafa25 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a4d9b6652b63eacd8c0ae1fb3a3eafa25">setIndices</a> (const boost::shared_ptr&lt; std::vector&lt; int &gt; &gt; &amp;indices)</td></tr>
<tr class="memdesc:a4d9b6652b63eacd8c0ae1fb3a3eafa25 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_sample_consensus_model.html#a4d9b6652b63eacd8c0ae1fb3a3eafa25">更多...</a><br /></td></tr>
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<tr class="memitem:a6d23c173237ca4be1451182b9a84b3ac inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a6d23c173237ca4be1451182b9a84b3ac">setIndices</a> (const std::vector&lt; int &gt; &amp;indices)</td></tr>
<tr class="memdesc:a6d23c173237ca4be1451182b9a84b3ac inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide the vector of indices that represents the input data.  <a href="classpcl_1_1_sample_consensus_model.html#a6d23c173237ca4be1451182b9a84b3ac">更多...</a><br /></td></tr>
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<tr class="memitem:a7b59467495bbcc1e6ba1b69bbd6c4206 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a7b59467495bbcc1e6ba1b69bbd6c4206"></a>
boost::shared_ptr&lt; std::vector&lt; int &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a7b59467495bbcc1e6ba1b69bbd6c4206">getIndices</a> () const</td></tr>
<tr class="memdesc:a7b59467495bbcc1e6ba1b69bbd6c4206 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the vector of indices used. <br /></td></tr>
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<tr class="memitem:a490884cb22bfa92325111bd5be0bbe96 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a490884cb22bfa92325111bd5be0bbe96"></a>
const std::string &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a490884cb22bfa92325111bd5be0bbe96">getClassName</a> () const</td></tr>
<tr class="memdesc:a490884cb22bfa92325111bd5be0bbe96 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a string representation of the name of this class. <br /></td></tr>
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<tr class="memitem:aff175b4323080fe1a3ca98a76ca4da46 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="aff175b4323080fe1a3ca98a76ca4da46"></a>
unsigned int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#aff175b4323080fe1a3ca98a76ca4da46">getSampleSize</a> () const</td></tr>
<tr class="memdesc:aff175b4323080fe1a3ca98a76ca4da46 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return the size of a sample from which the model is computed. <br /></td></tr>
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<tr class="memitem:a35884d269a39055c962b24d1db5fa97d inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a35884d269a39055c962b24d1db5fa97d"></a>
unsigned int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a35884d269a39055c962b24d1db5fa97d">getModelSize</a> () const</td></tr>
<tr class="memdesc:a35884d269a39055c962b24d1db5fa97d inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return the number of coefficients in the model. <br /></td></tr>
<tr class="separator:a35884d269a39055c962b24d1db5fa97d inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3ed94271a12d7903c0bee9091e9a8f95 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a3ed94271a12d7903c0bee9091e9a8f95">setRadiusLimits</a> (const double &amp;min_radius, const double &amp;max_radius)</td></tr>
<tr class="memdesc:a3ed94271a12d7903c0bee9091e9a8f95 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the minimum and maximum allowable radius limits for the model (applicable to models that estimate a radius)  <a href="classpcl_1_1_sample_consensus_model.html#a3ed94271a12d7903c0bee9091e9a8f95">更多...</a><br /></td></tr>
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<tr class="memitem:acff95be7097cf723a598d3b28e828487 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#acff95be7097cf723a598d3b28e828487">getRadiusLimits</a> (double &amp;min_radius, double &amp;max_radius)</td></tr>
<tr class="memdesc:acff95be7097cf723a598d3b28e828487 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the minimum and maximum allowable radius limits for the model as set by the user.  <a href="classpcl_1_1_sample_consensus_model.html#acff95be7097cf723a598d3b28e828487">更多...</a><br /></td></tr>
<tr class="separator:acff95be7097cf723a598d3b28e828487 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af9eea9dd0e12e1de8671ac542400d3f1 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#af9eea9dd0e12e1de8671ac542400d3f1">setSamplesMaxDist</a> (const double &amp;radius, SearchPtr search)</td></tr>
<tr class="memdesc:af9eea9dd0e12e1de8671ac542400d3f1 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the maximum distance allowed when drawing random samples  <a href="classpcl_1_1_sample_consensus_model.html#af9eea9dd0e12e1de8671ac542400d3f1">更多...</a><br /></td></tr>
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<tr class="memitem:a1aebacea0f2bdd7529f48f739f11c6c0 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a1aebacea0f2bdd7529f48f739f11c6c0">getSamplesMaxDist</a> (double &amp;radius)</td></tr>
<tr class="memdesc:a1aebacea0f2bdd7529f48f739f11c6c0 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get maximum distance allowed when drawing random samples  <a href="classpcl_1_1_sample_consensus_model.html#a1aebacea0f2bdd7529f48f739f11c6c0">更多...</a><br /></td></tr>
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<tr class="memitem:a2ac96856f60671f5d56be00a174963a8 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a2ac96856f60671f5d56be00a174963a8">computeVariance</a> (const std::vector&lt; double &gt; &amp;error_sqr_dists)</td></tr>
<tr class="memdesc:a2ac96856f60671f5d56be00a174963a8 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute the variance of the errors to the model.  <a href="classpcl_1_1_sample_consensus_model.html#a2ac96856f60671f5d56be00a174963a8">更多...</a><br /></td></tr>
<tr class="separator:a2ac96856f60671f5d56be00a174963a8 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad3de5bb66de04a4f0b3bc014a0676d48 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="ad3de5bb66de04a4f0b3bc014a0676d48"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#ad3de5bb66de04a4f0b3bc014a0676d48">computeVariance</a> ()</td></tr>
<tr class="memdesc:ad3de5bb66de04a4f0b3bc014a0676d48 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute the variance of the errors to the model from the internally estimated vector of distances. The model must be computed first (or at least selectWithinDistance must be called). <br /></td></tr>
<tr class="separator:ad3de5bb66de04a4f0b3bc014a0676d48 inherit pub_methods_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-methods"></a>
Protected 成员函数</h2></td></tr>
<tr class="memitem:aafc02bea3d135d035c3304ddb7bf694b"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html#aafc02bea3d135d035c3304ddb7bf694b">isSampleGood</a> (const std::vector&lt; int &gt; &amp;samples) const</td></tr>
<tr class="memdesc:aafc02bea3d135d035c3304ddb7bf694b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check if a sample of indices results in a good sample of points indices.  <a href="classpcl_1_1_sample_consensus_model_line.html#aafc02bea3d135d035c3304ddb7bf694b">更多...</a><br /></td></tr>
<tr class="separator:aafc02bea3d135d035c3304ddb7bf694b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_methods_classpcl_1_1_sample_consensus_model"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classpcl_1_1_sample_consensus_model')"><img src="closed.png" alt="-"/>&#160;Protected 成员函数 继承自 <a class="el" href="classpcl_1_1_sample_consensus_model.html">pcl::SampleConsensusModel&lt; PointT &gt;</a></td></tr>
<tr class="memitem:a910f3f0d75a6c2bf8bc4a9c43df825ce inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a910f3f0d75a6c2bf8bc4a9c43df825ce">SampleConsensusModel</a> (bool random=false)</td></tr>
<tr class="memdesc:a910f3f0d75a6c2bf8bc4a9c43df825ce inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty constructor for base <a class="el" href="classpcl_1_1_sample_consensus_model.html" title="SampleConsensusModel represents the base model class. All sample consensus models must inherit from t...">SampleConsensusModel</a>.  <a href="classpcl_1_1_sample_consensus_model.html#a910f3f0d75a6c2bf8bc4a9c43df825ce">更多...</a><br /></td></tr>
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<tr class="memitem:a2f687798633c8e702568aea8d68b2bec inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a2f687798633c8e702568aea8d68b2bec">drawIndexSample</a> (std::vector&lt; int &gt; &amp;sample)</td></tr>
<tr class="memdesc:a2f687798633c8e702568aea8d68b2bec inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a sample array with random samples from the indices_ vector  <a href="classpcl_1_1_sample_consensus_model.html#a2f687798633c8e702568aea8d68b2bec">更多...</a><br /></td></tr>
<tr class="separator:a2f687798633c8e702568aea8d68b2bec inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a94696e418be704a4443b031ca18bc298 inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a94696e418be704a4443b031ca18bc298">drawIndexSampleRadius</a> (std::vector&lt; int &gt; &amp;sample)</td></tr>
<tr class="memdesc:a94696e418be704a4443b031ca18bc298 inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a sample array with one random sample from the indices_ vector and other random samples that are closer than samples_radius_  <a href="classpcl_1_1_sample_consensus_model.html#a94696e418be704a4443b031ca18bc298">更多...</a><br /></td></tr>
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<tr class="memitem:ad22a9cfdd9cf08bcf85ef1e88b62b9d2 inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">virtual bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#ad22a9cfdd9cf08bcf85ef1e88b62b9d2">isModelValid</a> (const Eigen::VectorXf &amp;model_coefficients)</td></tr>
<tr class="memdesc:ad22a9cfdd9cf08bcf85ef1e88b62b9d2 inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check whether a model is valid given the user constraints.  <a href="classpcl_1_1_sample_consensus_model.html#ad22a9cfdd9cf08bcf85ef1e88b62b9d2">更多...</a><br /></td></tr>
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<tr class="memitem:ab8b77e6a4edb210c2ddaca923ac8470a inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="ab8b77e6a4edb210c2ddaca923ac8470a"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#ab8b77e6a4edb210c2ddaca923ac8470a">rnd</a> ()</td></tr>
<tr class="memdesc:ab8b77e6a4edb210c2ddaca923ac8470a inherit pro_methods_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Boost-based random number generator. <br /></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a>
额外继承的成员函数</h2></td></tr>
<tr class="inherit_header pro_attribs_classpcl_1_1_sample_consensus_model"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classpcl_1_1_sample_consensus_model')"><img src="closed.png" alt="-"/>&#160;Protected 属性 继承自 <a class="el" href="classpcl_1_1_sample_consensus_model.html">pcl::SampleConsensusModel&lt; PointT &gt;</a></td></tr>
<tr class="memitem:ab10fd6af7c7ab78040d4f638a572943f inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="ab10fd6af7c7ab78040d4f638a572943f"></a>
std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#ab10fd6af7c7ab78040d4f638a572943f">model_name_</a></td></tr>
<tr class="memdesc:ab10fd6af7c7ab78040d4f638a572943f inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">The model name. <br /></td></tr>
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<tr class="memitem:a7bffa6777a49f1c461ee8e0960bbec01 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a7bffa6777a49f1c461ee8e0960bbec01"></a>
PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a></td></tr>
<tr class="memdesc:a7bffa6777a49f1c461ee8e0960bbec01 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">A boost shared pointer to the point cloud data array. <br /></td></tr>
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<tr class="memitem:ad45adf0e36f396a908c2ba08cfdd7a41 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="ad45adf0e36f396a908c2ba08cfdd7a41"></a>
boost::shared_ptr&lt; std::vector&lt; int &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#ad45adf0e36f396a908c2ba08cfdd7a41">indices_</a></td></tr>
<tr class="memdesc:ad45adf0e36f396a908c2ba08cfdd7a41 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">A pointer to the vector of point indices to use. <br /></td></tr>
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<tr class="memitem:a3e4708339fa0bc73aac513ddb1891a18 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a3e4708339fa0bc73aac513ddb1891a18"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a3e4708339fa0bc73aac513ddb1891a18">radius_min_</a></td></tr>
<tr class="memdesc:a3e4708339fa0bc73aac513ddb1891a18 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">The minimum and maximum radius limits for the model. Applicable to all models that estimate a radius. <br /></td></tr>
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double&#160;</td><td class="memItemRight" valign="bottom"><b>radius_max_</b></td></tr>
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<tr class="memitem:aab89218101a313d2aa8adcaf6991f73b inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="aab89218101a313d2aa8adcaf6991f73b"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#aab89218101a313d2aa8adcaf6991f73b">samples_radius_</a></td></tr>
<tr class="memdesc:aab89218101a313d2aa8adcaf6991f73b inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">The maximum distance of subsequent samples from the first (radius search) <br /></td></tr>
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<tr class="memitem:a4b939f4747b5d6e12b03148a680380c3 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a4b939f4747b5d6e12b03148a680380c3"></a>
SearchPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a4b939f4747b5d6e12b03148a680380c3">samples_radius_search_</a></td></tr>
<tr class="memdesc:a4b939f4747b5d6e12b03148a680380c3 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">The search object for picking subsequent samples using radius search <br /></td></tr>
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<tr class="memitem:a9b8317cdaac5ccc14e822135d4e97dc1 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">std::vector&lt; int &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a9b8317cdaac5ccc14e822135d4e97dc1">shuffled_indices_</a></td></tr>
<tr class="separator:a9b8317cdaac5ccc14e822135d4e97dc1 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a73e9fb74bbe6ba122781c42913618756 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a73e9fb74bbe6ba122781c42913618756"></a>
boost::mt19937&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a73e9fb74bbe6ba122781c42913618756">rng_alg_</a></td></tr>
<tr class="memdesc:a73e9fb74bbe6ba122781c42913618756 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Boost-based random number generator algorithm. <br /></td></tr>
<tr class="separator:a73e9fb74bbe6ba122781c42913618756 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a81041dab94f5b0a5644697a12c4b9351 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a81041dab94f5b0a5644697a12c4b9351"></a>
boost::shared_ptr&lt; boost::uniform_int&lt;&gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a81041dab94f5b0a5644697a12c4b9351">rng_dist_</a></td></tr>
<tr class="memdesc:a81041dab94f5b0a5644697a12c4b9351 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Boost-based random number generator distribution. <br /></td></tr>
<tr class="separator:a81041dab94f5b0a5644697a12c4b9351 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7c57e94b06912d8bbf472667ba9940db inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a7c57e94b06912d8bbf472667ba9940db"></a>
boost::shared_ptr&lt; boost::variate_generator&lt; boost::mt19937 &amp;, boost::uniform_int&lt;&gt; &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a7c57e94b06912d8bbf472667ba9940db">rng_gen_</a></td></tr>
<tr class="memdesc:a7c57e94b06912d8bbf472667ba9940db inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">Boost-based random number generator. <br /></td></tr>
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<tr class="memitem:af526982856012edcfa78ffc0fd589f97 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="af526982856012edcfa78ffc0fd589f97"></a>
std::vector&lt; double &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#af526982856012edcfa78ffc0fd589f97">error_sqr_dists_</a></td></tr>
<tr class="memdesc:af526982856012edcfa78ffc0fd589f97 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">A vector holding the distances to the computed model. Used internally. <br /></td></tr>
<tr class="separator:af526982856012edcfa78ffc0fd589f97 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a64854cad238a0583093c446dc295f245 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="a64854cad238a0583093c446dc295f245"></a>
unsigned int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a64854cad238a0583093c446dc295f245">sample_size_</a></td></tr>
<tr class="memdesc:a64854cad238a0583093c446dc295f245 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">The size of a sample from which the model is computed. Every subclass should initialize this appropriately. <br /></td></tr>
<tr class="separator:a64854cad238a0583093c446dc295f245 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab7f51bf1c63fcfb05694d0f19c2f7dc3 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top"><a id="ab7f51bf1c63fcfb05694d0f19c2f7dc3"></a>
unsigned int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#ab7f51bf1c63fcfb05694d0f19c2f7dc3">model_size_</a></td></tr>
<tr class="memdesc:ab7f51bf1c63fcfb05694d0f19c2f7dc3 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="mdescLeft">&#160;</td><td class="mdescRight">The number of coefficients in the model. Every subclass should initialize this appropriately. <br /></td></tr>
<tr class="separator:ab7f51bf1c63fcfb05694d0f19c2f7dc3 inherit pro_attribs_classpcl_1_1_sample_consensus_model"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_static_attribs_classpcl_1_1_sample_consensus_model"><td colspan="2" onclick="javascript:toggleInherit('pro_static_attribs_classpcl_1_1_sample_consensus_model')"><img src="closed.png" alt="-"/>&#160;静态 Protected 属性 继承自 <a class="el" href="classpcl_1_1_sample_consensus_model.html">pcl::SampleConsensusModel&lt; PointT &gt;</a></td></tr>
<tr class="memitem:a19d4a467b5c0cd6267e6282ac0a2cf8c inherit pro_static_attribs_classpcl_1_1_sample_consensus_model"><td class="memItemLeft" align="right" valign="top">static const unsigned int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_sample_consensus_model.html#a19d4a467b5c0cd6267e6282ac0a2cf8c">max_sample_checks_</a> = 1000</td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;typename PointT&gt;<br />
class pcl::SampleConsensusModelLine&lt; PointT &gt;</h3>

<p><a class="el" href="classpcl_1_1_sample_consensus_model_line.html" title="SampleConsensusModelLine defines a model for 3D line segmentation. The model coefficients are defined...">SampleConsensusModelLine</a> defines a model for 3D line segmentation. The model coefficients are defined as: </p>
<ul>
<li><b>point_on_line.x</b> : the X coordinate of a point on the line</li>
<li><b>point_on_line.y</b> : the Y coordinate of a point on the line</li>
<li><b>point_on_line.z</b> : the Z coordinate of a point on the line</li>
<li><b>line_direction.x</b> : the X coordinate of a line's direction</li>
<li><b>line_direction.y</b> : the Y coordinate of a line's direction</li>
<li><b>line_direction.z</b> : the Z coordinate of a line's direction</li>
</ul>
<dl class="section author"><dt>作者</dt><dd>Radu B. Rusu </dd></dl>
</div><h2 class="groupheader">构造及析构函数说明</h2>
<a id="a790ec0b14c1f834f1d5377999bfb0d6d"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a790ec0b14c1f834f1d5377999bfb0d6d">&#9670;&nbsp;</a></span>SampleConsensusModelLine() <span class="overload">[1/2]</span></h2>

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<div class="memtemplate">
template&lt;typename PointT &gt; </div>
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          <td class="memname"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::<a class="el" href="classpcl_1_1_sample_consensus_model_line.html">SampleConsensusModelLine</a> </td>
          <td>(</td>
          <td class="paramtype">const PointCloudConstPtr &amp;&#160;</td>
          <td class="paramname"><em>cloud</em>, </td>
        </tr>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>random</em> = <code>false</code>&#160;</td>
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          <td>)</td>
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<p>Constructor for base <a class="el" href="classpcl_1_1_sample_consensus_model_line.html" title="SampleConsensusModelLine defines a model for 3D line segmentation. The model coefficients are defined...">SampleConsensusModelLine</a>. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">cloud</td><td>the input point cloud dataset </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">random</td><td>if true set the random seed to the current time, else set to 12345 (default: false) </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;        : SampleConsensusModel&lt;PointT&gt; (cloud, random)</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;      {</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;        <a class="code" href="classpcl_1_1_sample_consensus_model.html#ab10fd6af7c7ab78040d4f638a572943f">model_name_</a> = <span class="stringliteral">&quot;SampleConsensusModelLine&quot;</span>;</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;        <a class="code" href="classpcl_1_1_sample_consensus_model.html#a64854cad238a0583093c446dc295f245">sample_size_</a> = 2;</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;        <a class="code" href="classpcl_1_1_sample_consensus_model.html#ab7f51bf1c63fcfb05694d0f19c2f7dc3">model_size_</a> = 6;</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_html_a64854cad238a0583093c446dc295f245"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model.html#a64854cad238a0583093c446dc295f245">pcl::SampleConsensusModel::sample_size_</a></div><div class="ttdeci">unsigned int sample_size_</div><div class="ttdoc">The size of a sample from which the model is computed. Every subclass should initialize this appropri...</div><div class="ttdef"><b>Definition:</b> sac_model.h:572</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_html_ab10fd6af7c7ab78040d4f638a572943f"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model.html#ab10fd6af7c7ab78040d4f638a572943f">pcl::SampleConsensusModel::model_name_</a></div><div class="ttdeci">std::string model_name_</div><div class="ttdoc">The model name.</div><div class="ttdef"><b>Definition:</b> sac_model.h:534</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_html_ab7f51bf1c63fcfb05694d0f19c2f7dc3"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model.html#ab7f51bf1c63fcfb05694d0f19c2f7dc3">pcl::SampleConsensusModel::model_size_</a></div><div class="ttdeci">unsigned int model_size_</div><div class="ttdoc">The number of coefficients in the model. Every subclass should initialize this appropriately.</div><div class="ttdef"><b>Definition:</b> sac_model.h:575</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#abe1a6aab73b9d5674cccf1b042404975">&#9670;&nbsp;</a></span>SampleConsensusModelLine() <span class="overload">[2/2]</span></h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname"><a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::<a class="el" href="classpcl_1_1_sample_consensus_model_line.html">SampleConsensusModelLine</a> </td>
          <td>(</td>
          <td class="paramtype">const PointCloudConstPtr &amp;&#160;</td>
          <td class="paramname"><em>cloud</em>, </td>
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          <td></td>
          <td class="paramtype">const std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>indices</em>, </td>
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          <td></td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>random</em> = <code>false</code>&#160;</td>
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          <td>)</td>
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<p>Constructor for base <a class="el" href="classpcl_1_1_sample_consensus_model_line.html" title="SampleConsensusModelLine defines a model for 3D line segmentation. The model coefficients are defined...">SampleConsensusModelLine</a>. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">cloud</td><td>the input point cloud dataset </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">indices</td><td>a vector of point indices to be used from <em>cloud</em> </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">random</td><td>if true set the random seed to the current time, else set to 12345 (default: false) </td></tr>
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<div class="fragment"><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        : SampleConsensusModel&lt;PointT&gt; (cloud, indices, random)</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;      {</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;        <a class="code" href="classpcl_1_1_sample_consensus_model.html#ab10fd6af7c7ab78040d4f638a572943f">model_name_</a> = <span class="stringliteral">&quot;SampleConsensusModelLine&quot;</span>;</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        <a class="code" href="classpcl_1_1_sample_consensus_model.html#a64854cad238a0583093c446dc295f245">sample_size_</a> = 2;</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        <a class="code" href="classpcl_1_1_sample_consensus_model.html#ab7f51bf1c63fcfb05694d0f19c2f7dc3">model_size_</a> = 6;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;      }</div>
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<h2 class="groupheader">成员函数说明</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a2c09de6c3d95758f25c1942ac7a050f9">&#9670;&nbsp;</a></span>computeModelCoefficients()</h2>

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<div class="memtemplate">
template&lt;typename PointT &gt; </div>
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          <td class="memname">bool <a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::computeModelCoefficients </td>
          <td>(</td>
          <td class="paramtype">const std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>samples</em>, </td>
        </tr>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>model_coefficients</em>&#160;</td>
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<p>Check whether the given index samples can form a valid line model, compute the model coefficients from these samples and store them internally in model_coefficients_. The line coefficients are represented by a point and a line direction </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">samples</td><td>the point indices found as possible good candidates for creating a valid model </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">model_coefficients</td><td>the resultant model coefficients </td></tr>
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<p>实现了 <a class="el" href="classpcl_1_1_sample_consensus_model.html#a76c5d4484f1c08a9a15f03e497296cfe">pcl::SampleConsensusModel&lt; PointT &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;{</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;  <span class="comment">// Need 2 samples</span></div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  <span class="keywordflow">if</span> (samples.size () != 2)</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;  {</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::SampleConsensusModelLine::computeModelCoefficients] Invalid set of samples given (%lu)!\n&quot;</span>, samples.size ());</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;  }</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160; </div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;  <span class="keywordflow">if</span> (fabs (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[0]].x - <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[1]].x) &lt;= std::numeric_limits&lt;float&gt;::epsilon () &amp;&amp; </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;      fabs (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[0]].y - <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[1]].y) &lt;= std::numeric_limits&lt;float&gt;::epsilon () &amp;&amp; </div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;      fabs (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[0]].z - <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[1]].z) &lt;= std::numeric_limits&lt;float&gt;::epsilon ())</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;  {</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  }</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160; </div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;  model_coefficients.resize (6);</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;  model_coefficients[0] = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[0]].x;</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  model_coefficients[1] = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[0]].y;</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  model_coefficients[2] = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[0]].z;</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160; </div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;  model_coefficients[3] = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[1]].x - model_coefficients[0];</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  model_coefficients[4] = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[1]].y - model_coefficients[1];</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  model_coefficients[5] = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[1]].z - model_coefficients[2];</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160; </div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;  model_coefficients.template tail&lt;3&gt; ().normalize ();</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_html_a7bffa6777a49f1c461ee8e0960bbec01"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">pcl::SampleConsensusModel::input_</a></div><div class="ttdeci">PointCloudConstPtr input_</div><div class="ttdoc">A boost shared pointer to the point cloud data array.</div><div class="ttdef"><b>Definition:</b> sac_model.h:537</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a2885d3c4271f8cb8aed6ebc34c65d994">&#9670;&nbsp;</a></span>countWithinDistance()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">int <a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::countWithinDistance </td>
          <td>(</td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>model_coefficients</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const double&#160;</td>
          <td class="paramname"><em>threshold</em>&#160;</td>
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          <td>)</td>
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<p>Count all the points which respect the given model coefficients as inliers. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">model_coefficients</td><td>the coefficients of a model that we need to compute distances to </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">threshold</td><td>maximum admissible distance threshold for determining the inliers from the outliers </td></tr>
  </table>
  </dd>
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<dl class="section return"><dt>返回</dt><dd>the resultant number of inliers </dd></dl>

<p>实现了 <a class="el" href="classpcl_1_1_sample_consensus_model.html#a4257edca979112fb2a7b5dfc4b824ca2">pcl::SampleConsensusModel&lt; PointT &gt;</a>.</p>

<p>被 <a class="el" href="classpcl_1_1_sample_consensus_model_parallel_line.html#af0ead11670ea3b23befc3f6d8de20191">pcl::SampleConsensusModelParallelLine&lt; PointT &gt;</a> 重载.</p>
<div class="fragment"><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;{</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;  <span class="comment">// Needs a valid set of model coefficients</span></div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad22a9cfdd9cf08bcf85ef1e88b62b9d2">isModelValid</a> (model_coefficients))</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    <span class="keywordflow">return</span> (0);</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160; </div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;  <span class="keywordtype">double</span> sqr_threshold = threshold * threshold;</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160; </div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;  <span class="keywordtype">int</span> nr_p = 0;</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160; </div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;  <span class="comment">// Obtain the line point and direction</span></div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;  line_dir.normalize ();</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160; </div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;  <span class="comment">// Iterate through the 3d points and calculate the distances from them to the line</span></div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; <a class="code" href="classpcl_1_1_sample_consensus_model.html#ad45adf0e36f396a908c2ba08cfdd7a41">indices_</a>-&gt;size (); ++i)</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;  {</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <span class="comment">// Calculate the distance from the point to the line</span></div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    <span class="comment">// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)</span></div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keywordtype">double</span> sqr_distance = (line_pt - <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160; </div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <span class="keywordflow">if</span> (sqr_distance &lt; sqr_threshold)</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;      nr_p++;</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;  }</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;  <span class="keywordflow">return</span> (nr_p);</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_html_ad22a9cfdd9cf08bcf85ef1e88b62b9d2"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model.html#ad22a9cfdd9cf08bcf85ef1e88b62b9d2">pcl::SampleConsensusModel::isModelValid</a></div><div class="ttdeci">virtual bool isModelValid(const Eigen::VectorXf &amp;model_coefficients)</div><div class="ttdoc">Check whether a model is valid given the user constraints.</div><div class="ttdef"><b>Definition:</b> sac_model.h:516</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_html_ad45adf0e36f396a908c2ba08cfdd7a41"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model.html#ad45adf0e36f396a908c2ba08cfdd7a41">pcl::SampleConsensusModel::indices_</a></div><div class="ttdeci">boost::shared_ptr&lt; std::vector&lt; int &gt; &gt; indices_</div><div class="ttdoc">A pointer to the vector of point indices to use.</div><div class="ttdef"><b>Definition:</b> sac_model.h:540</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a46511a245836abd6d73fd924f2a4f285">&#9670;&nbsp;</a></span>doSamplesVerifyModel()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">bool <a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::doSamplesVerifyModel </td>
          <td>(</td>
          <td class="paramtype">const std::set&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>indices</em>, </td>
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          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>model_coefficients</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const double&#160;</td>
          <td class="paramname"><em>threshold</em>&#160;</td>
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          <td></td>
          <td>)</td>
          <td></td><td></td>
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<p>Verify whether a subset of indices verifies the given line model coefficients. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">indices</td><td>the data indices that need to be tested against the line model </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">model_coefficients</td><td>the line model coefficients </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">threshold</td><td>a maximum admissible distance threshold for determining the inliers from the outliers </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_sample_consensus_model.html#ab36a50dd4848b902f2e7a75b29b78c76">pcl::SampleConsensusModel&lt; PointT &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;{</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;  <span class="comment">// Needs a valid set of model coefficients</span></div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad22a9cfdd9cf08bcf85ef1e88b62b9d2">isModelValid</a> (model_coefficients))</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160; </div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;  <span class="comment">// Obtain the line point and direction</span></div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;  line_dir.normalize ();</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160; </div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;  <span class="keywordtype">double</span> sqr_threshold = threshold * threshold;</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;  <span class="comment">// Iterate through the 3d points and calculate the distances from them to the line</span></div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;  <span class="keywordflow">for</span> (std::set&lt;int&gt;::const_iterator it = indices.begin (); it != indices.end (); ++it)</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;  {</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <span class="comment">// Calculate the distance from the point to the line</span></div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    <span class="comment">// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)</span></div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    <span class="keywordflow">if</span> ((line_pt - <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[*it].getVector4fMap ()).cross3 (line_dir).squaredNorm () &gt; sqr_threshold)</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;      <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;  }</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160; </div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;  <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a68b282649799eac370616e9ab124c970">&#9670;&nbsp;</a></span>getDistancesToModel()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::getDistancesToModel </td>
          <td>(</td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>model_coefficients</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; double &gt; &amp;&#160;</td>
          <td class="paramname"><em>distances</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">virtual</span></span>  </td>
  </tr>
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</div><div class="memdoc">

<p>Compute all squared distances from the cloud data to a given line model. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">model_coefficients</td><td>the coefficients of a line model that we need to compute distances to </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">distances</td><td>the resultant estimated squared distances </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_sample_consensus_model.html#adc4ab752a4819218988ff5429b5a3c06">pcl::SampleConsensusModel&lt; PointT &gt;</a>.</p>

<p>被 <a class="el" href="classpcl_1_1_sample_consensus_model_parallel_line.html#aba35bb7eb6ca7b019983bbddd8573fb4">pcl::SampleConsensusModelParallelLine&lt; PointT &gt;</a> 重载.</p>
<div class="fragment"><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;{</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  <span class="comment">// Needs a valid set of model coefficients</span></div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad22a9cfdd9cf08bcf85ef1e88b62b9d2">isModelValid</a> (model_coefficients))</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160; </div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;  distances.resize (<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad45adf0e36f396a908c2ba08cfdd7a41">indices_</a>-&gt;size ());</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160; </div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;  <span class="comment">// Obtain the line point and direction</span></div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  line_dir.normalize ();</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160; </div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;  <span class="comment">// Iterate through the 3d points and calculate the distances from them to the line</span></div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; <a class="code" href="classpcl_1_1_sample_consensus_model.html#ad45adf0e36f396a908c2ba08cfdd7a41">indices_</a>-&gt;size (); ++i)</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;  {</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    <span class="comment">// Calculate the distance from the point to the line</span></div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="comment">// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)</span></div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="comment">// Need to estimate sqrt here to keep MSAC and friends general</span></div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    distances[i] = sqrt ((line_pt - <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[(*<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad45adf0e36f396a908c2ba08cfdd7a41">indices_</a>)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ());</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;  }</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aafc02bea3d135d035c3304ddb7bf694b">&#9670;&nbsp;</a></span>isSampleGood()</h2>

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<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">bool <a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::isSampleGood </td>
          <td>(</td>
          <td class="paramtype">const std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>samples</em></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">protected</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Check if a sample of indices results in a good sample of points indices. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">samples</td><td>the resultant index samples </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_sample_consensus_model.html#ac38e3dc90b66bacc563605a31901ac55">pcl::SampleConsensusModel&lt; PointT &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;{</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  <span class="keywordflow">if</span> (</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;      (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[0]].x != <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[1]].x)</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;    &amp;&amp;</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;      (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[0]].y != <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[1]].y)</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    &amp;&amp;</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;      (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[0]].z != <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[samples[1]].z))</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160; </div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ad29efa5e1fc75c814612f5c4926233ef">&#9670;&nbsp;</a></span>optimizeModelCoefficients()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::optimizeModelCoefficients </td>
          <td>(</td>
          <td class="paramtype">const std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>inliers</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>model_coefficients</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>optimized_coefficients</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Recompute the line coefficients using the given inlier set and return them to the user. </p>
<dl class="section note"><dt>注解</dt><dd>: these are the coefficients of the line model after refinement (e.g. after SVD) </dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">inliers</td><td>the data inliers found as supporting the model </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">model_coefficients</td><td>the initial guess for the model coefficients </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">optimized_coefficients</td><td>the resultant recomputed coefficients after optimization </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_sample_consensus_model.html#a667767897fbfe615aeeb9eca8dd1b8c6">pcl::SampleConsensusModel&lt; PointT &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;{</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;  <span class="comment">// Needs a valid set of model coefficients</span></div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad22a9cfdd9cf08bcf85ef1e88b62b9d2">isModelValid</a> (model_coefficients))</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  {</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    optimized_coefficients = model_coefficients;</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;  }</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160; </div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;  <span class="comment">// Need at least 2 points to estimate a line</span></div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;  <span class="keywordflow">if</span> (inliers.size () &lt;= 2)</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;  {</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::SampleConsensusModelLine::optimizeModelCoefficients] Not enough inliers found to support a model (%lu)! Returning the same coefficients.\n&quot;</span>, inliers.size ());</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    optimized_coefficients = model_coefficients;</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;  }</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160; </div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;  optimized_coefficients.resize (6);</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160; </div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;  <span class="comment">// Compute the 3x3 covariance matrix</span></div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;  Eigen::Vector4f centroid;</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;  <a class="code" href="group__common.html#gaf5729fae15603888b49743b118025290">compute3DCentroid</a> (*<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>, inliers, centroid);</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;  Eigen::Matrix3f covariance_matrix;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;  <a class="code" href="group__common.html#gac36b146ec26b1ceb7be43a9ecaa010c4">computeCovarianceMatrix</a> (*<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>, inliers, centroid, covariance_matrix);</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;  optimized_coefficients[0] = centroid[0];</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;  optimized_coefficients[1] = centroid[1];</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;  optimized_coefficients[2] = centroid[2];</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160; </div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;  <span class="comment">// Extract the eigenvalues and eigenvectors</span></div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;  EIGEN_ALIGN16 Eigen::Vector3f eigen_values;</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;  EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;  <a class="code" href="group__common.html#gaca873868052e7d26efcf4b684a17bef2">pcl::eigen33</a> (covariance_matrix, eigen_values);</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;  <a class="code" href="group__common.html#ga11c9b186d04d2e8a868e058473214622">pcl::computeCorrespondingEigenVector</a> (covariance_matrix, eigen_values [2], eigen_vector);</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;  <span class="comment">//pcl::eigen33 (covariance_matrix, eigen_vectors, eigen_values);</span></div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160; </div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;  optimized_coefficients.template tail&lt;3&gt; ().matrix () = eigen_vector;</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;}</div>
<div class="ttc" id="agroup__common_html_ga11c9b186d04d2e8a868e058473214622"><div class="ttname"><a href="group__common.html#ga11c9b186d04d2e8a868e058473214622">pcl::computeCorrespondingEigenVector</a></div><div class="ttdeci">void computeCorrespondingEigenVector(const Matrix &amp;mat, const typename Matrix::Scalar &amp;eigenvalue, Vector &amp;eigenvector)</div><div class="ttdoc">determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi defin...</div><div class="ttdef"><b>Definition:</b> eigen.hpp:219</div></div>
<div class="ttc" id="agroup__common_html_gac36b146ec26b1ceb7be43a9ecaa010c4"><div class="ttname"><a href="group__common.html#gac36b146ec26b1ceb7be43a9ecaa010c4">pcl::computeCovarianceMatrix</a></div><div class="ttdeci">unsigned int computeCovarianceMatrix(const pcl::PointCloud&lt; PointT &gt; &amp;cloud, const Eigen::Matrix&lt; Scalar, 4, 1 &gt; &amp;centroid, Eigen::Matrix&lt; Scalar, 3, 3 &gt; &amp;covariance_matrix)</div><div class="ttdoc">Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix...</div></div>
<div class="ttc" id="agroup__common_html_gaca873868052e7d26efcf4b684a17bef2"><div class="ttname"><a href="group__common.html#gaca873868052e7d26efcf4b684a17bef2">pcl::eigen33</a></div><div class="ttdeci">void eigen33(const Matrix &amp;mat, typename Matrix::Scalar &amp;eigenvalue, Vector &amp;eigenvector)</div><div class="ttdoc">determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...</div><div class="ttdef"><b>Definition:</b> eigen.hpp:251</div></div>
<div class="ttc" id="agroup__common_html_gaf5729fae15603888b49743b118025290"><div class="ttname"><a href="group__common.html#gaf5729fae15603888b49743b118025290">pcl::compute3DCentroid</a></div><div class="ttdeci">unsigned int compute3DCentroid(ConstCloudIterator&lt; PointT &gt; &amp;cloud_iterator, Eigen::Matrix&lt; Scalar, 4, 1 &gt; &amp;centroid)</div><div class="ttdoc">Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.</div><div class="ttdef"><b>Definition:</b> centroid.hpp:50</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#afc373efb74ec6e2c4761a5714b54b316">&#9670;&nbsp;</a></span>projectPoints()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::projectPoints </td>
          <td>(</td>
          <td class="paramtype">const std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>inliers</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>model_coefficients</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &amp;&#160;</td>
          <td class="paramname"><em>projected_points</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>copy_data_fields</em> = <code>true</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
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<span class="mlabels"><span class="mlabel">virtual</span></span>  </td>
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<p>Create a new point cloud with inliers projected onto the line model. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">inliers</td><td>the data inliers that we want to project on the line model </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">model_coefficients</td><td>the <em>normalized</em> coefficients of a line model </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">projected_points</td><td>the resultant projected points </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">copy_data_fields</td><td>set to true if we need to copy the other data fields </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_sample_consensus_model.html#a34c480d48be740e6085e8ba59c7293ed">pcl::SampleConsensusModel&lt; PointT &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;{</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;  <span class="comment">// Needs a valid model coefficients</span></div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad22a9cfdd9cf08bcf85ef1e88b62b9d2">isModelValid</a> (model_coefficients))</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160; </div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;  <span class="comment">// Obtain the line point and direction</span></div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160; </div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;  projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">header</a> = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;header;</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;  projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#a3ca88d8ebf6f4f35acbc31cdfb38aa94">is_dense</a> = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;is_dense;</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160; </div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;  <span class="comment">// Copy all the data fields from the input cloud to the projected one?</span></div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;  <span class="keywordflow">if</span> (copy_data_fields)</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;  {</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;    <span class="comment">// Allocate enough space and copy the basics</span></div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points.size ());</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a>    = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;width;</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;    projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a>   = <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;height;</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160; </div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code" href="structpcl_1_1traits_1_1field_list.html">pcl::traits::fieldList&lt;PointT&gt;::type</a> FieldList;</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;    <span class="comment">// Iterate over each point</span></div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (); ++i)</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;      <span class="comment">// Iterate over each dimension</span></div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;      pcl::for_each_type &lt;FieldList&gt; (NdConcatenateFunctor &lt;PointT, PointT&gt; (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[i], projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i]));</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160; </div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    <span class="comment">// Iterate through the 3d points and calculate the distances from them to the line</span></div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; inliers.size (); ++i)</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    {</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;      Eigen::Vector4f pt (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[inliers[i]].x, <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[inliers[i]].y, <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[inliers[i]].z, 0);</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;      <span class="comment">// double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;</span></div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;      <span class="keywordtype">float</span> k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160; </div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;      Eigen::Vector4f pp = line_pt + k * line_dir;</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;      <span class="comment">// Calculate the projection of the point on the line (pointProj = A + k * B)</span></div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;      projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[inliers[i]].x = pp[0];</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;      projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[inliers[i]].y = pp[1];</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;      projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[inliers[i]].z = pp[2];</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    }</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;  }</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;  {</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <span class="comment">// Allocate enough space and copy the basics</span></div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (inliers.size ());</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a>    = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (inliers.size ());</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a>   = 1;</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160; </div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code" href="structpcl_1_1traits_1_1field_list.html">pcl::traits::fieldList&lt;PointT&gt;::type</a> FieldList;</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    <span class="comment">// Iterate over each point</span></div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; inliers.size (); ++i)</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;      <span class="comment">// Iterate over each dimension</span></div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;      pcl::for_each_type &lt;FieldList&gt; (NdConcatenateFunctor &lt;PointT, PointT&gt; (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[inliers[i]], projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i]));</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160; </div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    <span class="comment">// Iterate through the 3d points and calculate the distances from them to the line</span></div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; inliers.size (); ++i)</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    {</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;      Eigen::Vector4f pt (<a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[inliers[i]].x, <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[inliers[i]].y, <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[inliers[i]].z, 0);</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;      <span class="comment">// double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;</span></div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;      <span class="keywordtype">float</span> k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160; </div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;      Eigen::Vector4f pp = line_pt + k * line_dir;</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;      <span class="comment">// Calculate the projection of the point on the line (pointProj = A + k * B)</span></div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;      projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].x = pp[0];</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;      projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].y = pp[1];</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;      projected_points.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].z = pp[2];</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    }</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;  }</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a2185a6453f8ad905d7bdf7b45754a160"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">pcl::PointCloud::width</a></div><div class="ttdeci">uint32_t width</div><div class="ttdoc">The point cloud width (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:413</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a3ca88d8ebf6f4f35acbc31cdfb38aa94"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a3ca88d8ebf6f4f35acbc31cdfb38aa94">pcl::PointCloud::is_dense</a></div><div class="ttdeci">bool is_dense</div><div class="ttdoc">True if no points are invalid (e.g., have NaN or Inf values).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:418</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a4f34b45220c57f96607513ffad0d9582"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">pcl::PointCloud::height</a></div><div class="ttdeci">uint32_t height</div><div class="ttdoc">The point cloud height (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:415</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a82e0be055a617e5e74102ed62712b352"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">pcl::PointCloud::header</a></div><div class="ttdeci">pcl::PCLHeader header</div><div class="ttdoc">The point cloud header. It contains information about the acquisition time.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:407</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_af16a62638198313b9c093127c492c884"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">pcl::PointCloud::points</a></div><div class="ttdeci">std::vector&lt; PointT, Eigen::aligned_allocator&lt; PointT &gt; &gt; points</div><div class="ttdoc">The point data.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:410</div></div>
<div class="ttc" id="astructpcl_1_1traits_1_1field_list_html"><div class="ttname"><a href="structpcl_1_1traits_1_1field_list.html">pcl::traits::fieldList</a></div><div class="ttdef"><b>Definition:</b> point_traits.h:177</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a1204c9998433ce3b8b1e20240085df66">&#9670;&nbsp;</a></span>selectWithinDistance()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_sample_consensus_model_line.html">pcl::SampleConsensusModelLine</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::selectWithinDistance </td>
          <td>(</td>
          <td class="paramtype">const Eigen::VectorXf &amp;&#160;</td>
          <td class="paramname"><em>model_coefficients</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const double&#160;</td>
          <td class="paramname"><em>threshold</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>inliers</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">virtual</span></span>  </td>
  </tr>
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</div><div class="memdoc">

<p>Select all the points which respect the given model coefficients as inliers. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">model_coefficients</td><td>the coefficients of a line model that we need to compute distances to </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">threshold</td><td>a maximum admissible distance threshold for determining the inliers from the outliers </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">inliers</td><td>the resultant model inliers </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_sample_consensus_model.html#af9aefc3c3ab13ede1430c680e9243d26">pcl::SampleConsensusModel&lt; PointT &gt;</a>.</p>

<p>被 <a class="el" href="classpcl_1_1_sample_consensus_model_parallel_line.html#ae3077204ccbc9222490bbe724fa39070">pcl::SampleConsensusModelParallelLine&lt; PointT &gt;</a> 重载.</p>
<div class="fragment"><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;{</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;  <span class="comment">// Needs a valid set of model coefficients</span></div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad22a9cfdd9cf08bcf85ef1e88b62b9d2">isModelValid</a> (model_coefficients))</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160; </div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;  <span class="keywordtype">double</span> sqr_threshold = threshold * threshold;</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160; </div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;  <span class="keywordtype">int</span> nr_p = 0;</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;  inliers.resize (<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad45adf0e36f396a908c2ba08cfdd7a41">indices_</a>-&gt;size ());</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;  <a class="code" href="classpcl_1_1_sample_consensus_model.html#af526982856012edcfa78ffc0fd589f97">error_sqr_dists_</a>.resize (<a class="code" href="classpcl_1_1_sample_consensus_model.html#ad45adf0e36f396a908c2ba08cfdd7a41">indices_</a>-&gt;size ());</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160; </div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;  <span class="comment">// Obtain the line point and direction</span></div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;  line_dir.normalize ();</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160; </div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;  <span class="comment">// Iterate through the 3d points and calculate the distances from them to the line</span></div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; <a class="code" href="classpcl_1_1_sample_consensus_model.html#ad45adf0e36f396a908c2ba08cfdd7a41">indices_</a>-&gt;size (); ++i)</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;  {</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    <span class="comment">// Calculate the distance from the point to the line</span></div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    <span class="comment">// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)</span></div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    <span class="keywordtype">double</span> sqr_distance = (line_pt - <a class="code" href="classpcl_1_1_sample_consensus_model.html#a7bffa6777a49f1c461ee8e0960bbec01">input_</a>-&gt;points[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160; </div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    <span class="keywordflow">if</span> (sqr_distance &lt; sqr_threshold)</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    {</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;      <span class="comment">// Returns the indices of the points whose squared distances are smaller than the threshold</span></div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;      inliers[nr_p] = (*indices_)[i];</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus_model.html#af526982856012edcfa78ffc0fd589f97">error_sqr_dists_</a>[nr_p] = sqr_distance;</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;      ++nr_p;</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    }</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;  }</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;  inliers.resize (nr_p);</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;  <a class="code" href="classpcl_1_1_sample_consensus_model.html#af526982856012edcfa78ffc0fd589f97">error_sqr_dists_</a>.resize (nr_p);</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_html_af526982856012edcfa78ffc0fd589f97"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model.html#af526982856012edcfa78ffc0fd589f97">pcl::SampleConsensusModel::error_sqr_dists_</a></div><div class="ttdeci">std::vector&lt; double &gt; error_sqr_dists_</div><div class="ttdoc">A vector holding the distances to the computed model. Used internally.</div><div class="ttdef"><b>Definition:</b> sac_model.h:569</div></div>
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<li>sample_consensus/include/pcl/sample_consensus/<a class="el" href="sac__model__line_8h_source.html">sac_model_line.h</a></li>
<li>sample_consensus/include/pcl/sample_consensus/impl/<a class="el" href="sac__model__line_8hpp_source.html">sac_model_line.hpp</a></li>
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